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Agentforce for Retail

Agentforce for Retail

Salesforce Introduces Agentforce for Retail and Retail Cloud with Modern POS Just ahead of the National Retail Federation (NRF) conference in New York, Salesforce has announced Agentforce for Retail and Retail Cloud with Modern POS, further expanding its AI-driven capabilities into the retail sector. This launch underscores Salesforce’s commitment to leveraging agentic AI to enhance productivity and streamline retail operations. Saks Fifth Avenue and SharkNinja: Early Adopters Retail giants like Saks Fifth Avenue and SharkNinja are among the first to test Agentforce for Retail, showcasing its potential to redefine customer engagement and operational efficiency in the industry. Agentforce for Retail Agentforce for Retail equips businesses with a library of pre-built AI agent skills tailored specifically for retail use cases, delivering what Salesforce calls “digital labor.” These skills are designed to simplify routine tasks, freeing up human employees to focus on higher-value activities. New Pre-Built Skills These tools aim to help retailers deploy AI agents efficiently, improving customer satisfaction and operational scalability. Retail Cloud with Modern POS Retail Cloud with Modern POS consolidates online and offline inventory data into a single, unified platform, empowering retailers with modern tools for seamless customer experiences. Key Features Retail Cloud integrates seamlessly with Commerce Cloud, Service Cloud, and Order Management, ensuring a cohesive ecosystem for retailers. Why This Matters This dual announcement highlights Salesforce’s strategy to embed Agentforce as a foundational capability across its multi-cloud architecture, while also tailoring solutions for specific industries like retail. “Together, AI-fueled digital labor and a modern POS can unlock a new scale of operational capacity for retailers,” said Nitin Mangtani, Salesforce SVP and GM of Retail, in a press release. With Agentforce for Retail, Salesforce empowers brands to embrace AI-driven automation while modernizing their point-of-sale systems, enabling a seamless blend of personalized customer engagement and operational excellence. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Agentic AI is Here

On Premise Gen AI

In 2025, enterprises transitioning generative AI (GenAI) into production after years of experimentation are increasingly considering on-premises deployment as a cost-effective alternative to the cloud. Since OpenAI ignited the AI revolution in late 2022, organizations have tested large language models powering GenAI services on platforms like AWS, Microsoft Azure, and Google Cloud. These experiments demonstrated GenAI’s potential to enhance business operations while exposing the substantial costs of cloud usage. To avoid difficult conversations with CFOs about escalating cloud expenses, CIOs are exploring on-premises AI as a financially viable solution. Advances in software from startups and packaged infrastructure from vendors such as HPE and Dell are making private data centers an attractive option for managing costs. A survey conducted by Menlo Ventures in late 2024 found that 47% of U.S. enterprises with at least 50 employees were developing GenAI solutions in-house. Similarly, Informa TechTarget’s Enterprise Strategy Group reported a rise in enterprises considering on-premises and public cloud equally for new applications—from 37% in 2024 to 45% in 2025. This shift is reflected in hardware sales. HPE reported a 16% revenue increase in AI systems, reaching $1.5 billion in Q4 2024. During the same period, Dell recorded a record $3.6 billion in AI server orders, with its sales pipeline expanding by over 50% across various customer segments. “Customers are seeking diverse AI-capable server solutions,” noted David Schmidt, senior director of Dell’s PowerEdge server line. While heavily regulated industries have traditionally relied on on-premises systems to ensure data privacy and security, broader adoption is now driven by the need for cost control. Fortune 2000 companies are leading this trend, opting for private infrastructure over the cloud due to more predictable expenses. “It’s not unusual to see cloud bills exceeding 0,000 or even million per month,” said John Annand, an analyst at Info-Tech Research Group. Global manufacturing giant Jabil primarily uses AWS for GenAI development but emphasizes ongoing cost management. “Does moving to the cloud provide a cost advantage? Sometimes it doesn’t,” said CIO May Yap. Jabil employs a continuous cloud financial optimization process to maximize efficiency. On-Premises AI: Technology and Trends Enterprises now have alternatives to cloud infrastructure, including as-a-service solutions like Dell APEX and HPE GreenLake, which offer flexible pay-per-use pricing for AI servers, storage, and networking tailored for private data centers or colocation facilities. “The high cost of cloud drives organizations to seek more predictable expenses,” said Tiffany Osias, vice president of global colocation services at Equinix. Walmart exemplifies in-house AI development, creating tools like a document summarization app for its benefits help desk and an AI assistant for corporate employees. Startups are also enabling enterprises to build AI applications with turnkey solutions. “About 80% of GenAI requirements can now be addressed with push-button solutions from startups,” said Tim Tully, partner at Menlo Ventures. Companies like Ragie (RAG-as-a-service) and Lamatic.ai (GenAI platform-as-a-service) are driving this innovation. Others, like Squid AI, integrate custom AI agents with existing enterprise infrastructure. Open-source frameworks like LangChain further empower on-premises development, offering tools for creating chatbots, virtual assistants, and intelligent search systems. Its extension, LangGraph, adds functionality for building multi-agent workflows. As enterprises develop AI applications internally, consulting services will play a pivotal role. “Companies offering guidance on effective AI tool usage and aligning them with business outcomes will thrive,” Annand said. This evolution in AI deployment highlights the growing importance of balancing technological innovation with financial sustainability. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI evolves with tools like Agentforce and Atlas

Agentforce Powered Marketing

Maximize Team Productivity and Customer Engagement with Agentforce and AI-Powered Marketing Tools Transform your marketing operations with Agentforce, an advanced AI-powered suite seamlessly integrated into your platform. From building end-to-end campaigns to personalizing touchpoints in real time, Agentforce empowers your team to optimize performance with actionable AI insights. Here’s how: Revolutionize Campaign Management with Agentforce Agent-Driven Campaign Briefs Streamline campaign creation with Agentforce, which uses structured and unstructured data from Data Cloud to create tailored campaign briefs. Define your target segments and key messages effortlessly with the support of AI. AI-Powered Content Creation Leverage Agentforce to generate on-brand content at scale, including email subject lines, body copy, and SMS messages. Every piece of content aligns with your brand guidelines and campaign goals, ensuring consistency and relevance across audiences. Unified SMS Conversations Turn static promotions into dynamic, two-way conversations with Agentforce Unified SMS. Automatically connect customers to AI agents for tasks like appointment scheduling and offer redemption, delivering seamless customer experiences. Supercharge Insights and Actions with Data Cloud Agent-Driven AI Segmentation Create target audience segments in minutes using natural language prompts. With Agentforce and Data Cloud working in harmony, agents translate prompts into precise segment attributes—no technical expertise or SQL required. Integrate or Build Custom AI Models Develop predictive AI models with clicks, not code, or bring in existing models via direct integrations with tools like Amazon SageMaker, Google Vertex AI, or Databricks. Use these models to generate actionable predictions, such as purchase propensity or churn likelihood. Secure, Harmonized Data Foundation Keep your data safe on the Einstein Trust Layer while enabling agents to analyze harmonized, structured, and unstructured data in Data Cloud. This ensures informed decision-making without compromising security. Automate Intelligent Journeys with Marketing Cloud Engagement Journey Optimization Automate personalized campaign variations with predictive AI. Optimize engagement by tailoring content, timing, channels, and frequency dynamically across customer journeys. Generative AI for Content Creation Solve the content bottleneck with generative AI tools that instantly create on-brand copy and visuals grounded in first-party data, campaign insights, and brand guidelines—all while safeguarding trust. Real-Time Messaging Insights Stay proactive with Einstein Messaging Insights, which flags engagement anomalies like sudden drops in click-through rates. These real-time insights enable quick resolutions, preventing performance surprises. Unified WhatsApp Conversations Transform WhatsApp into a dynamic two-way engagement channel. Use a single WhatsApp number to connect marketing and service teams while enabling AI-driven self-service actions like appointment booking and offer redemptions. Scale Lead Generation and Account-Based Marketing Agent-Driven Campaign Creation Accelerate campaign planning with Agentforce, which handles everything from briefs to audience segmentation, content, and journey creation. Ground campaigns in real-time customer data for accurate targeting, all with marketer oversight for approvals. AI Lead and Account Scoring Boost alignment between marketing and sales with Einstein AI Scoring, which identifies top leads and prospects automatically. Improve ABM strategies with automated account rankings based on historical and behavioral data, driving higher conversions. Full-Funnel Attribution Gain end-to-end visibility with AI-powered multi-touch attribution. Use models like Einstein Attribution to measure the impact of each channel, event, or team activity on your pipeline, boosting ROI and campaign efficiency. Personalization on Auto-Pilot with AI Objective-Based AI Recommendations Set business objectives and let AI optimize product and content recommendations to achieve those goals. AI-Automated Offers Combine real-time customer behavior data with AI-driven insights to personalize offers across touchpoints. This results in higher satisfaction and conversion rates tailored to each individual customer. Real-Time Affinity Profiling Use AI to uncover customer affinities, preferences, and intent in real time. Deliver hyper-personalized messaging and offers across your website, app, and other channels for maximum engagement. Optimize Spend, Planning, and Performance with Marketing Cloud Intelligence AI-Powered Data Integration Say goodbye to spreadsheets and manual data maintenance. Automate data unification, KPI standardization, and cross-channel analytics with AI-powered connectors, saving time and boosting campaign effectiveness. AI Campaign Performance Insights Get interactive visualizations and AI-generated insights to adjust campaign spend and offers mid-flight. Use these insights to optimize ROI and maximize in-the-moment opportunities. Predictive Budgeting and Planning Allocate budgets more effectively with predictive AI. Real-time alerts help prevent overages or underspending, ensuring your marketing dollars are used efficiently for maximum return. With Agentforce and AI marketing tools, your team can focus on what matters most—building stronger customer relationships and driving measurable results. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Salesforce and AWS-Agentic Enterprise

Salesforce and AWS: Driving the Future of the Agentic Enterprise As AI-powered agents redefine the way businesses operate, strategic partnerships are playing a pivotal role in harnessing the power of data and artificial intelligence. Salesforce and AWS, two industry leaders, have taken significant steps toward building a smarter, agentic enterprise through their expanded collaboration. One year into this strategic partnership, their joint efforts are delivering transformative AI and data solutions, helping customers like Buyers Edge Platform unlock new efficiencies and capabilities. A Partnership Fueling Agentic AI Salesforce and AWS are aligning their AI and data initiatives to pave the way for advanced agentic systems—autonomous AI agents designed to enhance business operations and customer experiences. Among their notable achievements over the past year are: These innovations are creating an ecosystem that supports the delivery of agentic AI, enabling businesses to streamline operations and tap into new value from their data. “By integrating data and AI capabilities across our platforms, Salesforce and AWS are building a strong foundation for the future of agentic systems,” said Brian Landsman, EVP of Global Business Development and Technology Partnerships at Salesforce. “With a majority of large companies planning to implement agents by 2027, organizations need trusted partners to help them achieve their vision of a smarter enterprise.” Making AI More Accessible Salesforce is simplifying access to AI technology through the AWS Marketplace, offering customers an integrated solution that includes Agentforce—the agentic layer of the Salesforce platform. Agentforce enables businesses to deploy autonomous AI agents across various operations, streamlining workflows and delivering measurable results. Available in 23 countries, Salesforce’s presence on AWS Marketplace offers customers key advantages, including: By removing barriers to adoption, Salesforce and AWS empower companies to focus on leveraging technology for growth rather than navigating complex procurement systems. A New Era of Enterprise Efficiency As businesses increasingly rely on data and AI to remain competitive, the Salesforce-AWS partnership is setting the stage for enterprises to achieve more with agentic systems. These systems allow companies to execute complex tasks with unprecedented efficiency, maximizing ROI on technology investments. “Our partnership with Salesforce empowers mutual customers to realize the full potential of their data and AI investments,” said Chris Grusz, Managing Director of Technology Partnerships at AWS. “Together, we’re delivering immediate, actionable insights with agentic AI, enabling organizations to automate strategically and unlock more value across their operations.” Looking Ahead By seamlessly integrating data and AI capabilities, Salesforce and AWS are not just building technology solutions—they’re reshaping how enterprises operate and thrive in the digital age. As agentic AI becomes an essential part of business strategy, this partnership provides a blueprint for leveraging technology to drive smarter, more agile, and more effective enterprises. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Autonomy, Architecture, and Action

Redefining AI Agents: Autonomy, Architecture, and Action AI agents are reshaping how technology interacts with us and executes tasks. Their mission? To reason, plan, and act independently—following instructions, making autonomous decisions, and completing actions, often without user involvement. These agents adapt to new information, adjust in real time, and pursue their objectives autonomously. This evolution in agentic AI is revolutionizing how goals are accomplished, ushering in a future of semi-autonomous technology. At their foundation, AI agents rely on one or more large language models (LLMs). However, designing agents is far more intricate than building chatbots or generative assistants. While traditional AI applications often depend on user-driven inputs—such as prompt engineering or active supervision—agents operate autonomously. Core Principles of Agentic AI Architectures To enable autonomous functionality, agentic AI systems must incorporate: Essential Infrastructure for AI Agents Building and deploying agentic AI systems requires robust software infrastructure that supports: Agent Development Made Easier with Langflow and Astra DB Langflow simplifies the development of agentic applications with its visual IDE. It integrates with Astra DB, which combines vector and graph capabilities for ultra-low latency data access. This synergy accelerates development by enabling: Transforming Autonomy into Action Agentic AI is fundamentally changing how tasks are executed by empowering systems to act autonomously. By leveraging platforms like Astra DB and Langflow, organizations can simplify agent design and deploy scalable, effective AI applications. Start building the next generation of AI-powered autonomy today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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From Chatbots to Agentic AI

From Chatbots to Agentic AI

The transition from LLM-powered chatbots to agentic systems, or agentic AI, can be summed up by the old saying: “Less talk, more action.” Keeping up with advancements in AI can be overwhelming, especially when managing an existing business. The speed and complexity of innovation can make it feel like the first day of school all over again. This insight offers a comprehensive look at AI agents, their components, and key characteristics. The introductory section breaks down the elements that form the term “AI agent,” providing a clear definition. After establishing this foundation, we explore the evolution of LLM applications, particularly the shift from traditional chatbots to agentic systems. The goal is to understand why AI agents are becoming increasingly vital in AI development and how they differ from LLM-powered chatbots. By the end of this guide, you will have a deeper understanding of AI agents, their potential applications, and their impact on organizational workflows. For those of you with a technical background who prefer to get hands-on, click here for the best repository for AI developers and builders. What is an AI Agent? Components of AI Agents To understand the term “AI agent,” we need to examine its two main components. First, let’s consider artificial intelligence, or AI. Artificial Intelligence (AI) refers to non-biological intelligence that mimics human cognition to perform tasks traditionally requiring human intellect. Through machine learning and deep learning techniques, algorithms—especially neural networks—learn patterns from data. AI systems are used for tasks such as detection, classification, and prediction, with content generation becoming a prominent domain due to transformer-based models. These systems can match or exceed human performance in specific scenarios. The second component is “agent,” a term commonly used in both technology and human contexts. In computer science, an agent refers to a software entity with environmental awareness, able to perceive and act within its surroundings. A computational agent typically has the ability to: In human contexts, an agent is someone who acts on behalf of another person or organization, making decisions, gathering information, and facilitating interactions. They often play intermediary roles in transactions and decision-making. To define an AI agent, we combine these two perspectives: it is a computational entity with environmental awareness, capable of perceiving inputs, acting with tools, and processing information using foundation models backed by both long-term and short-term memory. Key Components and Characteristics of AI Agents From LLMs to AI Agents Now, let’s take a step back and understand how we arrived at the concept of AI agents, particularly by looking at how LLM applications have evolved. The shift from traditional chatbots to LLM-powered applications has been rapid and transformative. Form Factor Evolution of LLM Applications Traditional Chatbots to LLM-Powered Chatbots Traditional chatbots, which existed before generative AI, were simpler and relied on heuristic responses: “If this, then that.” They followed predefined rules and decision trees to generate responses. These systems had limited interactivity, with the fallback option of “Speak to a human” for complex scenarios. LLM-Powered Chatbots The release of OpenAI’s ChatGPT on November 30, 2022, marked the introduction of LLM-powered chatbots, fundamentally changing the game. These chatbots, like ChatGPT, were built on GPT-3.5, a large language model trained on massive datasets. Unlike traditional chatbots, LLM-powered systems can generate human-like responses, offering a much more flexible and intelligent interaction. However, challenges remained. LLM-powered chatbots struggled with personalization and consistency, often generating plausible but incorrect information—a phenomenon known as “hallucination.” This led to efforts in grounding LLM responses through techniques like retrieval-augmented generation (RAG). RAG Chatbots RAG is a method that combines data retrieval with LLM generation, allowing systems to access real-time or proprietary data, improving accuracy and relevance. This hybrid approach addresses the hallucination problem, ensuring more reliable outputs. LLM-Powered Chatbots to AI Agents As LLMs expanded, their abilities grew more sophisticated, incorporating advanced reasoning, multi-step planning, and the use of external tools (function calling). Tool use refers to an LLM’s ability to invoke specific functions, enabling it to perform more complex tasks. Tool-Augmented LLMs and AI Agents As LLMs became tool-augmented, the emergence of AI agents followed. These agents integrate reasoning, planning, and tool use into an autonomous, goal-driven system that can operate iteratively within a dynamic environment. Unlike traditional chatbot interfaces, AI agents leverage a broader set of tools to interact with various systems and accomplish tasks. Agentic Systems Agentic systems—computational architectures that include AI agents—embody these advanced capabilities. They can autonomously interact with systems, make decisions, and adapt to feedback, forming the foundation for more complex AI applications. Components of an AI Agent AI agents consist of several key components: Characteristics of AI Agents AI agents are defined by the following traits: Conclusion AI agents represent a significant leap from traditional chatbots, offering greater autonomy, complexity, and interactivity. However, the term “AI agent” remains fluid, with no universal industry standard. Instead, it exists on a continuum, with varying degrees of autonomy, adaptability, and proactive behavior defining agentic systems. Value and Impact of AI Agents The key benefits of AI agents lie in their ability to automate manual processes, reduce decision-making burdens, and enhance workflows in enterprise environments. By “agentifying” repetitive tasks, AI agents offer substantial productivity gains and the potential to transform how businesses operate. As AI agents evolve, their applications will only expand, driving new efficiencies and enabling organizations to leverage AI in increasingly sophisticated ways. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Speed to Launch of Agentforce

Speed to Launch of Agentforce

Agentforce isn’t just another AI platform that requires months of customization. At most customers, they quickly saw its power, launching transformative generative AI experiences in just days—no AI engineers needed. For companies with larger admin teams, the benefits can be even greater. Unlike other platforms, Agentforce places a strong emphasis on data privacy, building on the trust that Salesforce is known for, making these virtual assistants invaluable. We began with employee-facing use cases, saving our team several hours per week. Now, with Agentforce, we’re seeing even more opportunities to drive efficiencies and better serve our customers. “We’re excited to leverage Agentforce to completely overhaul recruitment and enrollment at Unity Environmental University. Instead of traditional forms or chatbots, our students will soon engage with an autonomous recruitment agent directly on our website, offering personalized support throughout the college application process.”– Dr. Melik Khoury, President & CEO, Unity Environmental University “For first-generation college students, the 1:385 coach-to-student ratio makes personalized guidance challenging. By integrating Agentforce into our platform, we’re deploying cutting-edge solutions to better support students. These agents enable our coaches to focus on high-touch, personalized experiences while handling vital tasks like sharing deadlines and answering common questions—24/7.”– Siva Kumari, CEO, College Possible “Agentforce offers organizations a unique opportunity to move beyond incremental improvements and achieve exponential ROI. By automating customer interactions, improving outcomes, and reducing costs, it integrates data, flows, and user interfaces to mitigate risks and accelerate value creation. This agent-based platform approach allows businesses to harness AI’s full potential, revolutionizing customer engagement and paving the way for exponential growth.”– Rebecca Wettemann, CEO and Principal Analyst, Valoir “Autonomous agents powered by Salesforce’s Agentforce are revolutionizing customer experiences by providing fast, accurate, and personalized support around the clock. With advanced AI making decisions and taking actions autonomously, businesses can resolve customer issues more efficiently, fostering deeper interactions and enhancing satisfaction. This innovation enables companies to reallocate human resources to more complex tasks, boosting individual productivity and scaling business growth. Agentforce is setting new standards for seamless sales, service, marketing, and commerce interactions, reinforcing its leadership in customer experience.”– Michael Fauscette, CEO and Chief Analyst, Arion Research LLC “The best way to predict the future is to invent it.” — Alan Kay, Computer Science Pioneer Technology progresses in what biologists call punctuated equilibrium, with new capabilities slowly emerging from labs and tinkerers until a breakthrough shifts the axis of possibility. These pioneering feats create new paradigms, unleashing waves of innovation—much like the Apple Macintosh, the iPhone, and the Salesforce Platform, which revolutionized the enterprise software-as-a-service (SaaS) model and sparked an entire industry. The Age of Agentforce Begins At Dreamforce 2024, Salesforce Futures reflected on the launch of Agentforce, inspired by visions like the Apple Knowledge Navigator. In 2023, we used this inspiration to craft our Salesforce 2030 film, which showcased the collaboration between humans and autonomous AI agents. Now, with Agentforce, we’re witnessing that vision come to life. Agentforce is a suite of customizable AI agents and tools built on the Salesforce Platform, offering an elegant solution to the complexity of AI deployment. It addresses the challenges of integrating data, models, infrastructure, and applications into a unified system. With powerful tools like Agent Builder and Model Builder, organizations can easily create, customize, and deploy AI agents. Salesforce’s Atlas Reasoning Engine empowers these agents to handle both routine and complex tasks autonomously. A New Era of AI Innovation At Dreamforce 2024, over 10,000 attendees raced to build their own agents using the “Agent Builder” experience, turning verbal instructions into fully functioning agents in under 15 minutes. This wasn’t just another chatbot—it’s a new breed of AI that could transform how businesses operate and deliver superior customer experiences. Companies like Saks, OpenTable, and Wiley have quickly embraced this technology. As Mick Costigan and David Berthy of Salesforce Futures explain, “When we see signals like this, it pushes us toward the future. Soon, we’ll see complex, multi-agent systems solving higher-order challenges, both in the enterprise and in consumer devices.” Shaping the Future Agentforce isn’t just a product—it’s a platform for experimentation. With hundreds of thousands of Salesforce customers soon gaining access, the full potential of these tools will unfold in ways we can’t yet imagine. As with every major technological shift, the real magic will lie in how people use it. Every interaction, every agent deployed, and every problem solved will shape the future in unexpected ways. Platform Evolution Adam Evans, Salesforce SVP of Product, notes that Agentforce builds on the company’s transformation over the past four years, following the pattern of Salesforce’s original disruption of enterprise software. Unlike traditional solutions, Agentforce eliminates the need for customers to build their own AI infrastructure, providing a ready-to-use solution. At the core of Agentforce is the Atlas Reasoning Engine, delivering results that are twice as relevant and 33% more accurate than competing solutions. This engine integrates Salesforce Data Cloud, Flow for automation, and the Einstein Trust Layer for governance. Early Customer Results Early Agentforce deployments highlight how organizations are using autonomous agents to enhance, rather than replace, human workers: George Pokorny, Senior VP of Global Customer Success at OpenTable, shared, “Just saving two minutes on a ten-minute call lets our service reps focus on strengthening customer relationships, thanks to seamless integration with Service Cloud, giving us a unified view of diner preferences and history.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Agentic AI is Here

Agentic AI Revolution

The Agentic AI Revolution: Lead, Follow, or Get Out of the Way The era of agentic AI is here, and the message is clear—if you’re not leading the charge, you’re falling behind. Companies like Wiley and OpenTable are reshaping their industries with autonomous AI agents that don’t just assist but also analyze, strategize, and execute tasks with unparalleled efficiency. As these organizations demonstrate, the key to AI success lies in rewriting the rules of your industry rather than playing catch-up. Rewriting Industry Standards with Agentic AI Wiley: The education giant leveraged Agentforce, a digital labor platform for deploying autonomous AI agents, to transform its customer service operations. By onboarding representatives 50% faster and improving case resolution by 40%, Wiley streamlined its processes in just a few weeks. AI agents now handle registration and payment inquiries, directing students to resources and reducing the workload on human representatives. OpenTable: As the go-to reservation platform for 1.7 billion diners annually, OpenTable deploys AI agents to manage reservation changes and loyalty points. This allows employees to focus on customer relationships. Even a two-minute efficiency gain per interaction translates to massive operational savings. Salesforce Help Site: With over 60 million annual visits, the Salesforce Help site integrated Agentforce to resolve 83% of queries without human involvement. In just weeks, Agentforce doubled its capacity, handling over 32,000 automated conversations. These examples showcase a new era of digital labor where AI agents orchestrate high-value, multistep tasks, working tirelessly to deliver results. Far from replacing humans, they supercharge productivity and innovation, enabling companies to do more than ever before. How to Empower Your Workforce with AI Empowering your workforce for the next wave of AI doesn’t require months of preparation or millions of dollars. You don’t need to build or train your own large language model (LLM). Instead, integrating AI with existing data, automation, and workflows is the key to success, as demonstrated by leaders like Wiley and OpenTable. Here’s how to get started: 1. Real-Time Data Access AI thrives on real-time, high-quality data. Platforms like Salesforce Data Cloud unify structured and unstructured data, connecting it seamlessly to the LLM. Techniques such as retrieval-augmented generation (RAG) and semantic search ensure AI agents can access the most relevant data for any task. 2. Advanced Reasoning AI agents aren’t just about answering queries—they execute complex, multistep tasks. For example, they can process returns, reorder items, and even flag anomalies. Powered by reasoning engines, these agents draw data from systems like CRM, refine plans, and adapt dynamically until the task is completed correctly. 3. Built-In Security AI agents must operate within clear guardrails, knowing their limits and handing tasks off to humans when necessary. Strong permissions and security protocols are essential to ensure data protection and prevent unauthorized actions. 4. Action-Oriented Workflows Generative AI’s real value lies in action. By integrating tools like Salesforce Flow for task automation and MuleSoft APIs for system connectivity, AI agents can execute business workflows such as fraud detection, customer outreach, and case management. 5. Human-AI Collaboration The future of work isn’t AI replacing humans—it’s AI and humans working together. While agents handle data-intensive and repetitive tasks, humans bring strategic thinking, empathy, and creativity. This synergy leads to smarter decisions and redefines workflows across industries. Why Training Your Own LLM May Not Be the Answer Many companies assume training a proprietary LLM will give them a competitive edge. In reality, this process is costly, time-intensive, and requires constant updates to remain accurate. An LLM trained on static data quickly becomes outdated, much like a GPS that fails after the first detour. Instead, companies are turning to out-of-the-box AI solutions that integrate seamlessly with their existing systems. These tools offer the flexibility to scale quickly and adapt in real time, enabling businesses to stay competitive without the heavy lift of building from scratch. Scaling AI for the Future Many organizations remain stuck in pilot phases with AI due to data quality issues and a limited understanding of use cases. Companies like Wiley and OpenTable, however, have cracked the code: integrating prebuilt AI systems with robust data flows, automation, and workflows. By embracing agentic AI, forward-thinking organizations are creating digital labor forces that unlock new efficiencies, enhance customer experiences, and position themselves for long-term success. The trillion-dollar AI opportunity awaits—will you lead or trail behind? Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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2024 The Year of Generative AI

Was 2024 the Year Generative AI Delivered? Here’s What Happened Industry experts hailed 2024 as the year generative AI would take center stage. Operational use cases were emerging, technology was simplifying access, and general artificial intelligence felt imminent. So, how much of that actually came true? Well… sort of. As the year wraps up, some predictions have hit their mark, while others — like general AI — remain firmly in development. Let’s break down the trends, insights from investor Tomasz Tunguz, and what’s ahead for 2025. 1. A World Without Reason Three years into our AI evolution, businesses are finding value, but not universally. Tomasz Tunguz categorizes AI’s current capabilities into: While prediction and search have gained traction, reasoning models still struggle. Why? Model accuracy. Tunguz notes that unless a model has repeatedly seen a specific pattern, it falters. For example, an AI generating an FP&A chart might succeed — but introduce a twist, like usage-based billing, and it’s lost. For now, copilots and modestly accurate search reign supreme. 2. Process Over Tooling A tool’s value lies in how well it fits into established processes. As data teams adopt AI, they’re realizing that production-ready AI demands robust processes, not just shiny tools. Take data quality — a critical pillar for AI success. Sampling a few dbt tests or point solutions won’t cut it anymore. Teams need comprehensive solutions that deliver immediate value. In 2025, expect a shift toward end-to-end platforms that simplify incident management, enhance data quality ownership, and enable domain-level solutions. The tools that integrate seamlessly and address these priorities will shape AI’s future. 3. AI: Cost Cutter, Not Revenue Generator For now, AI’s primary business value lies in cost reduction, not revenue generation. Tools like AI-driven SDRs can increase sales pipelines, but often at the cost of quality. Instead, companies are leveraging AI to cut costs in areas like labor. Examples include Klarna reducing two-thirds of its workforce and Microsoft boosting engineering productivity by 50-75%. Cost reduction works best in scenarios with repetitive tasks, hiring challenges, or labor shortages. Meanwhile, specialized services like EvenUp, which automates legal demand letters, show potential for revenue-focused AI use cases. 4. A Slower but Smarter Adoption Curve While 2023 saw a wave of experimentation with AI, 2024 marked a period of reflection. Early adopters have faced challenges with implementation, ROI, and rapidly changing tech. According to Tunguz, this “dress rehearsal” phase has informed organizations about what works and what doesn’t. Heading into 2025, expect a more calculated wave of AI adoption, with leaders focusing on tools that deliver measurable value — and faster. 5. Small Models for Big Gains In enterprise AI, small, fine-tuned models are gaining favor over massive, general-purpose ones. Why? Small models are cheaper to run and often outperform their larger counterparts when fine-tuned for specific tasks. For example, training an 8-billion-parameter model on 10,000 support tickets can yield better results than a general model trained on a broad corpus. Legal and cost challenges surrounding large proprietary models further push enterprises toward smaller, open-source solutions, especially in highly regulated industries. 6. Blurring Lines Between Analysts and Engineers The demand for data and AI solutions is driving a shift in responsibilities. AI-enabled pipelines are lowering barriers to entry, making self-serve data workflows more accessible. This trend could consolidate analytical and engineering roles, streamlining collaboration and boosting productivity in 2025. 7. Synthetic Data: A Necessary Stopgap With finite real-world training data, synthetic datasets are emerging as a stopgap solution. Tools like Tonic and Gretel create synthetic data for AI training, particularly in regulated industries. However, synthetic data has limits. Over time, relying too heavily on it could degrade model performance, akin to a diet lacking fresh nutrients. The challenge will be finding a balance between real and synthetic data as AI advances. 8. The Rise of the Unstructured Data Stack Unstructured data — long underutilized — is poised to become a cornerstone of enterprise AI. Only about half of unstructured data is analyzed today, but as AI adoption grows, this figure will rise. Organizations are exploring tools and strategies to harness unstructured data for training and analytics, unlocking its untapped potential. 2025 will likely see the emergence of a robust “unstructured data stack” designed to drive business value from this vast, underutilized resource. 9. Agentic AI: Not Ready for Prime Time While AI copilots have proven useful, multi-step AI agents still face significant challenges. Due to compounding accuracy issues (e.g., 90% accuracy over three steps drops to ~50%), these agents are not yet ready for production use. For now, agentic AI remains more of a conversation piece than a practical tool. 10. Data Pipelines Are Growing, But Quality Isn’t As enterprises scale their AI efforts, the number of data pipelines is exploding. Smaller, fine-tuned models are being deployed at scale, often requiring hundreds of millions of pipelines. However, this rapid growth introduces data quality risks. Without robust quality management practices, teams risk inconsistent outputs, bottlenecks, and missed opportunities. Looking Ahead to 2025 As AI evolves, enterprises will face growing pains, but the opportunities are undeniable. From streamlining processes to leveraging unstructured data, 2025 promises advancements that will redefine how organizations approach AI and data strategy. The real challenge? Turning potential into measurable, lasting impact. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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1 Billion Enterprise AI Agents

Inside Salesforce’s Ambition to Deploy 1 Billion Enterprise AI Agents Salesforce is making a bold play in the enterprise AI space with its recently launched Agentforce platform. Introduced at the annual Dreamforce conference, Agentforce is positioned to revolutionize sales, marketing, commerce, and operations with autonomous AI agents, marking a significant evolution from Salesforce’s previous Einstein AI platform. What Makes Agentforce Different? Agentforce operates as more than just a chatbot platform. It uses real-time data and user-defined business rules to proactively manage tasks, aiming to boost efficiency and enhance customer satisfaction. Built on Salesforce’s Data Cloud, the platform simplifies deployment while maintaining powerful customization capabilities: “Salesforce takes care of 80% of the foundational work, leaving customers to focus on the 20% that truly differentiates their business,” explains Adam Forrest, SVP of Marketing at Salesforce. Forrest highlights how Agentforce enables businesses to build custom agents tailored to specific needs by incorporating their own rules and data sources. This user-centric approach empowers admins, developers, and technology teams to deploy AI without extensive technical resources. Early Adoption Across Industries Major brands have already adopted Agentforce for diverse use cases: These real-world applications illustrate Agentforce’s potential to transform workflows in industries ranging from retail to hospitality and education. AI Agents in Marketing: The New Frontier Salesforce emphasizes that Agentforce isn’t just for operations; it’s poised to redefine marketing. AI agents can automate lead qualification, optimize outreach strategies, and enhance personalization. For example, in account-based marketing, agents can analyze customer data to identify high-value opportunities, craft tailored strategies, and recommend optimal engagement times based on user behavior. “AI agents streamline lead qualification by evaluating intent signals and scoring leads, allowing sales teams to focus on high-priority prospects,” says Jonathan Franchell, CEO of B2B marketing agency Ironpaper. Once campaigns are launched, Agentforce monitors performance in real time, offering suggestions to improve ROI and resource allocation. By integrating seamlessly with CRM platforms, the tool also facilitates better collaboration between marketing and sales teams. Beyond B2C applications, AI agents in B2B contexts can evaluate customer-specific needs and provide tailored product or service recommendations, further enhancing client relationships. Enabling Creativity Through Automation By automating repetitive tasks, Agentforce aims to free marketers to focus on strategy and creativity. Dan Gardner, co-founder of Code and Theory, describes this vision: “Agentic AI eliminates friction and dissolves silos in data, organizational structures, and customer touchpoints. The result? Smarter insights, efficient distribution, and more time for creatives to do what they do best: creating.” Competitive Landscape and Challenges Despite its promise, Salesforce faces stiff competition. Microsoft—backed by its integration with OpenAI’s ChatGPT—has unveiled AI tools like Copilot, and other players such as Google, ServiceNow, and HubSpot are advancing their own AI platforms. Salesforce CEO Marc Benioff has not shied away from the rivalry. On the Masters of Scale podcast, he criticized Microsoft for overpromising on products like Copilot, asserting that Salesforce delivers tangible value: “Our tools show users exactly what is possible, what is real, and how easy it is to derive huge value from AI.” Salesforce must also demonstrate Agentforce’s scalability across diverse industries to capture a significant share of the enterprise AI market. A Transformative Vision for the Future Agentforce represents Salesforce’s commitment to bringing AI-powered automation to the forefront of enterprise operations. With its focus on seamless deployment, powerful customization, and real-time capabilities, the platform aims to reshape how businesses interact with customers and optimize internal processes. By targeting diverse use cases and emphasizing accessibility for both technical and non-technical users, Salesforce is betting on Agentforce to drive adoption at scale—and position itself as a leader in the increasingly competitive AI market. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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New Service Cloud Tools

Service Cloud for HR

Salesforce has expanded its Service Cloud capabilities to include a new HR-focused solution, Employee Service, designed to streamline employee support and enhance productivity. Employee Service introduces a dedicated HR service console paired with an employee portal. This portal acts as a centralized hub for staff to access HR resources, offering instant answers via Generative AI (GenAI), direct communication with HR specialists across multiple channels, and self-service options for tasks like requesting paid time off (PTO). For HR teams, the service console consolidates employee data, case details, and a company’s knowledge base into a unified workspace. It leverages AI-driven tools to resolve cases faster, automate routine tasks, and deliver seamless employee experiences. Salesforce’s Agentforce customers can integrate AI agents into Employee Service to further automate processes, saving time and reducing repetitive workloads. In a LinkedIn announcement, Kishan Chetan, EVP and GM for Service Cloud, highlighted the solution’s potential: “This new solution unifies employee data, case details, and a company’s corporate knowledge base all in one workspace that gives HR teams a 360-degree view of each employee and the ability to manage employee support cases with built-in AI and productivity tools. HR teams can efficiently resolve employee issues using Agentforce to quickly search, respond, summarize, and close cases, extending teams to get work done faster.” Salesforce’s broader goal is to eliminate the reliance on fragmented HR tools and reduce the need for employees to navigate disparate platforms like email, internal systems, and collaboration tools to complete HR-related tasks. By doing so, Salesforce aims to simplify HR processes, minimize manual effort, and enhance overall productivity. Early adopters of Employee Service are already reporting significant results. According to Sherin Sunny, Sr. Director of Product Management at Salesforce, customers have observed a 31% increase in employee productivity. This aligns with broader trends: Recognizing the need for a unified HR ecosystem, Salesforce includes a prebuilt MuleSoft integration with Workday and configurable connectors to other Human Capital Management (HCM) systems. These integrations establish a centralized HR data foundation, reducing inefficiencies caused by siloed tools. Looking ahead, Beth Schultz, VP of Research & Principal Analyst at Metrigy, emphasized the importance of integrating Employee Service with Slack, Salesforce’s collaboration platform: “We’ll be particularly watching how Salesforce’s multifaceted plans for bringing [Employee Service] into Slack play out as Slack evolves into a fully connected, collaborative workspace.” Slack itself is undergoing a transformation, with Salesforce Co-Founder Patrick Harris returning to revamp the platform as a core part of the Salesforce ecosystem. Meanwhile, Salesforce continues to expand Service Cloud’s offerings beyond Employee Service. Recent developments include a revamped CCaaS (Contact Center as a Service) integration program and a new product discovery tool. Still, Agentforce remains a key focus for Salesforce’s marketing efforts, showcasing its potential to redefine how businesses deploy autonomous AI agents across use cases like HR and beyond. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI Sales Agents Explained

AI Sales Agents Explained

If you were to ask a sales rep why they chose a job in sales, they’d probably tell you something like, “I love helping to people. I’m ambitious and goal-oriented, and no two days are ever the same.” The reality, however, is that a lot of time in sales isn’t spent selling. Recent data suggests that sales reps dedicate only 28% of their time to actual selling, with the rest swallowed up by administrative tasks and non-revenue-generating work. To ease this burden, sales teams are turning to AI sales agents, enabling them to focus more on building relationships and closing deals. Below, we explore the different types of AI sales agents and how businesses are using them to increase productivity, efficiency, and revenue. What is an AI sales agent? AI sales agents are autonomous applications that analyze and learn from sales and customer data to perform tasks with little or no human intervention. These agents can manage a wide range of activities, from top-of-funnel tasks like nurturing leads via email outreach, answering questions, booking meetings, and generating quotes to more integrated sales support like buyer roleplays and coaching. Unlike simple workflow automation, AI agents are capable of learning, enabling them to improve efficiency and act independently based on data and analysis. They often plug directly into existing CRMs, with pre-built capabilities or customizable configurations for specific business needs. Types of AI sales agents There are two primary types of AI sales agents: The ability to autonomously analyze data, create action plans, and execute them sets modern AI sales agents apart from traditional sales tools and bots. Key features of AI sales agents Benefits of AI sales agents Future trends for AI sales agents In the early days, AI in sales served primarily as a co-pilot — summarizing insights and assisting with tasks like forecasting. It often required significant human input and created siloed data challenges. Today, AI agents autonomously augment human teams, empowering them to focus on high-value tasks like building relationships. In the near future, AI sales agents are expected to handle increasingly complex workflows and multi-step processes across diverse channels. Potential advancements include: These developments promise to unlock new possibilities for efficiency, personalization, and customization in sales teams. AI sales agents pushing teams into a new era According to recent data, sales leaders are focusing on improving sales enablement, targeting new markets, and adopting new tools and technologies to drive growth. Challenges like scaling personalized interactions and hitting quotas are top of mind. AI sales agents directly address these needs, transforming sales organizations by enabling teams to offload repetitive work to autonomous systems while maintaining quality and personalization. Who uses AI sales agents? AI sales agents are used by sales teams to manage tasks such as lead qualification, follow-ups, meeting scheduling, and coaching. By handling repetitive activities, these agents free up reps to focus on relationship-building and closing deals, ultimately driving better outcomes for both teams and customers. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI Agents Set to Break Through in 2025

AI Agents Set to Break Through in 2025

2025: The Year AI Agents Transform Work and Life Despite years of hype around artificial intelligence, its true disruptive impact has so far been limited. However, industry experts believe that’s about to change in 2025 as autonomous AI agents prepare to enter and reshape nearly every facet of our lives. Since OpenAI’s ChatGPT took the world by storm in late 2022, billions of dollars have been funneled into the AI sector. Big tech and startups alike are racing to harness the transformative potential of the technology. Yet, while millions now interact with AI chatbots daily, turning them into tools that deliver tangible business value has proven challenging. A recent study by Boston Consulting Group revealed that only 26% of companies experimenting with AI have progressed beyond proof of concept to derive measurable value. This lag reflects the limitations of current AI tools, which serve primarily as copilots—capable of assisting but requiring constant oversight and remaining prone to errors. AI Agents Set to Break Through in 2025 The status quo, however, is poised for a radical shift. Autonomous AI agents—capable of independently analyzing information, making decisions, and taking action—are expected to emerge as the industry’s next big breakthrough. “For the first time, technology isn’t just offering tools for humans to do work,” Salesforce CEO Marc Benioff wrote in Time. “It’s providing intelligent, scalable digital labor that performs tasks autonomously. Instead of waiting for human input, agents can analyze information, make decisions, and adapt as they go.” At their core, AI agents leverage the same large language models (LLMs) that power tools like ChatGPT. But these agents take it further, acting as reasoning engines that develop step-by-step strategies to execute tasks. Armed with access to external data sources like customer records or financial databases and equipped with software tools, agents can achieve goals independently. While current LLMs still face reasoning limitations, advancements are on the horizon. New models like OpenAI’s “o1” and DeepSeek’s “R1” are specialized for reasoning, sparking hope that 2025 will see agents grow far more capable. Big Tech and Startups Betting Big Major players are already gearing up for this new era. Startups are also eager to carve out their share of the market. According to Pitchbook, funding deals for agent-focused ventures surged by over 80% in 2024, with the median deal value increasing nearly 50%. Challenges to Overcome Despite the enthusiasm, significant hurdles remain. 2025: A Turning Point Despite these challenges, many experts believe 2025 will mark the mainstream adoption of AI agents. A New World of Work No matter the pace, it’s clear that AI agents will dominate the industry’s focus in 2025. If the technology delivers on its promise, the workplace could undergo a profound transformation, enabling entirely new ways of working and automating tasks that once required human intervention. The question isn’t if agents will redefine the way we work—it’s how fast. By the end of 2025, the shift could be undeniable. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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